II. expansion of the web and related technologies

II. expansion of the web and related technologies

II. EXISTING
MODEL

Data
mining is a distinct technique for data analysis which does not concentrate
upon purely descriptive purposes, rather, focuses on modelling and discovery of
knowledge for predictive purposes. Data relying excessively on aggregation and
aiming in business information comes under business intelligence. Customer data
and IT tools build the substructure on which a victorious CRM strategy is created.
Also, the quick expansion of the web and related technologies has substantially
extended the number of marketing opportunities. In addition, this has altered
the way relationships between

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Predictive analytics
aims at application of statistical models for estimating or categorization, while statistical,
language-producing, and systemic techniques are applied to text analysis to
acquire and classify information from textual resources.

Retrieving
information from the web incorporates handling the abstractness and volume of
data contained on the internet. When including factors such as word ambiguity and
a large number of typographical errors, it is made increasingly difficult. As estimated,
one word in every two-hundred words, on a web site, will contain a textual
error on an average. There exist a variety of key pitfalls comprehending IR- relevance,
evaluation, and information needs.

However, this is
not the complete set of issues involving IR. Common information retrieval
problems include potential, scalability and paging update occurrences. The
relational value of an input given by user in the form of query, within a
dataset, is known as relevance. This is generally based on a document ranking algorithm.

The larger complications
with web information retrieval that are relevance and evaluation are still significant
subject matters that require attention, amongst others.

The documents
and queries are a collection of terms where every term within the document is
indexed. 1 and 0 denote the presence and absence of some text in a text source
respectively 4,5. Maintenance of an inverted index of every term is necessary
in order to process matching of document and query. However, the Boolean model has
some major limitations as explained further. Binary decision criterion has a
disadvantage that it exists without any notion of grading scale. Another
includes overloading of documents. Some researchers have worked upon this to
overcome the weaknesses of the above said Boolean model by building improvisations
to the existing one. Certain researches have also approached data analysis with
a different search strategy known as the Vector Space model 5.

The Vector Space
Model represents documents and queries internally as vectors. In this model,
every query and document is represented as vectors that exist in a
|V|-dimensional space. Here V is the collection of all distinct terms in the set
of documents. Here, the documents set is the vocabulary 5.

Markov Processes
were first proposed by Russian Mathematician Andrei Markov. A Markov model in
probability theory is a stochastic model. This model is used to model systems
that change randomly. In this model, it is presumed that the future states depend
only on the present ones rather than the sequence of events that occurred prior
to it 1, 2, 6.

There exist four
Markov models that are used in different situations, depending on the
observational degree of every sequential state.

III. MODEL TO BE IMPLEMENTED

A hidden Markov
model (HMM) constitutes of a Markov model that is statistical. Here, the system
to be modelled is presumed to be a Markovian process with states that are hidden,
which implies that the states are unobserved. The simplest dynamic Bayesian
network can refer to as HMM 7.

For
the measurement of effectiveness of spontaneous information retrieval in the
standard way, we require a collection of tests consisting of three things:

? Collection of documents

? Test suite of requirements represented
as a set of queries.

? Set of conclusions, which standardly
is a binary assessment of relevance computed as either relevant or irrelevant
for every text-query pair.

Earlier, various
researchers have used following parameters to evaluate the performance of IR
Systems:

1.
Precision: It is the fraction of documents relevant among the completly
retrieved document. Practically it gives accuracy of the judgement.

Precision=|Ra|/|A|

2.
Recall: The fraction of the documents retrieved and relevant among all relevant
documents is referred to as recall. Practically it gives coverage of result.

Recall
=|Ra|/|R|

Where,

Ra:
Set of relevant documents retrieved

R:
Set of all relevant documents

In pattern
recognition system and IR with binary classification, precision refers to the
fraction of instances retrieved that are found to be relevant, while recall
refers to the fraction of relevant instances that are extracted and retrieved.
Both precision and recall are henceforth derived from an understanding and
degree of relevance.

A. Language used- Python

For both small
and large scale, Python helps enabling clear programs by providing constructs.
Its features include a dynamic type system and an automatic memory management.
It also has a huge and all-inclusive standard library.

Python’s large
standard library provides tools to users that are suited for numerous tasks.
Modules for creating GUIs, connecting them to relational databases, pseudorandom
number generators, and arithmetic decimals with arbitrary precision, manipulation
of regular expressions are included. It is also capable of performing unit
testing.

B. Dataset used

The
OHSUMED test collection is a combinational set of 348,566 references from
MEDLINE. It is the on-line database for medical information present on World
Wide Web. It has a title, MeSH indexing terms, author, and an abstract with
source as available fields in the database.

The existing
OHSUMED topics define the real requirements. Although, the judgements of
relevance does not have the same coverage as given by the pooling process of
TREC. The information requirements aren’t directly expressed by MeSH but these
terms manage the indexing terms. The standard TREC format provides the topic
statements and includes only

and fields.

The
relevant document files are described below which simulate human judgement and
contain information for 0 or 1 for every MeSH term expressed in the filtration
of any given topic.

(1)
OHSUMED
relevance judgments (files: qrels.ohsu.*)

Four
searchers replicate each query. Out of these four, two are physicians who are experienced
in searching and the other two are medical librarians. A completely different set
of physicians estimate the results for relevance. This judgement is performed
on a three point scale. The pointers are: definitely, possibly, or not
relevant. Consideration for relevance is done for all documents that are
checked to be either definitely relevant or possibly relevant.

(2)
MeSH
relevance judgments (files: qrels.mesh.*)

The document is considered to be relevant to a
MeSH topic if its concept is included in the list of MeSH term fields.

C. WHOOSH: Python Library

Whoosh was
created by Matt Chaput.

? Whoosh uses only pure python hence
runs anywhere python can, and so is fast. It runs without requiring a compiler.

? Whoosh uses the Okapi BM25F ranking
function by default, but can be easily modified.

? Fairly small indexes are created by
Whoosh as compared to numerous other search libraries.

It
is used for indexing the text and storing locations for the terms. These
positions or locations further allow phrase searching.

whoosh.fields.ID

The
entire value of the field is indexed into a single unit using the ID field,
rather than breaking it up into separate terms.

whoosh.fields.STORED

It
is neither an indexed type nor a searchable one. This is useful for displaying
the information to the user in the search results.

whoosh.fields.KEYWORD

An
indexed and searchable type, this is created for comma and space separated words.

whoosh.fields.NUMERIC

This
is capable of storing int, long, or floating point numbers in a format that is
sortable and compact

whoosh.fields.BOOLEAN

Indexing
of boolean values is done by this field and this type allows users to search
for results like: true, false, 1, 0, t, f, yes, no.

whoosh.fields.DATETIME

Date-time
objects are stored in this field in a compact and extremely sortable format.

A Format object is
made to define the type of information is recorded by a field about each term.
It also describes how it has to be stored on the disk. For example, this is how
the postings are stored by the Existence format:

While on the
other hand, this is how the Positions format would do the same:

The Unicode
string is passed to the field’s format object for a field by the indexing code.
An analyser is called by the format object which breaks the string into tokens.
Further, encoding of the information is done about each of them.

The inverted
index performs mapping of the terms to the documents in which they appear. Also,
sometimes it is useful to store a term vector that maps all the terms that arise
in the documents to the original document sources.

For example, inverted
index of a field is:For the image above, the respective
forward index is:

D. Creating An Index
Object

For
opening an existing index in a directory, index.open_dir:

import
whoosh.index as index

ix
= index.open_dir(“indexdir”)

For
creating an index in a directory, index.create_in:

import
os, os.path

from
whoosh import index

if
not os.path.exists(“indexdir”):

os.mkdir(“indexdir”)

ix
= index.create_in(“indexdir”, schema)

The
schema using which the index is created is stored with the index itself. Indexes
can be kept in the same directory using the index-name keyword.

To
use the convenience functions

ix
= index.create_in(“indexdir”, schema=schema, indexname=”usages”)

ix
= index.open_dir(“indexdir”, indexname=”usages”)

To
use the Storage object

ix
= storage.create_index(schema, indexname=”usages”)

ix
= storage.open_index(indexname=”usages”)

The relevance of
the documents using Hidden Markov Model is compared with the tf.idf approach.
Tf.idf is an approach based on numerical statistic based vector model. It reflects
necessity of a word to a document in a corpus. Often, it is used in IR and data
mining as a weighting factor.

The tf-idf value
is proportional to the frequency of appearance of a word given in the document.
Although, it is offset by the frequency of the word in the corpus. This helps
to relate to the fact that in general some words have more frequency of
appearance than others.

For the
implementation, the first step is to design the schema and then indexing is
performed 5. Then tf.idf values are calculated using Whoosh Library in Python.
For HMM calculation the data observed is assumed to be the query Q, and an
unknown key is assumed to be a relevant document D that is desired. The mind of
the user is a noisy channel, who is having either some precise or rough notion of
the documents he requires. This channel transforms that expressed notion into
the query text Q. Hence, we compute the probability for each document D that it
was the relevant one in the user mind, provided that Q was the query which was
expressed or produced, i.e. P (D is RjQ). We further rank the documents with respect
to this measure 6. This can be incorporated in the form of graphs. These graphical structures represent information
about a domain that is uncertain. Particularly, each node represents a random
variable and the edges denote the probabilistic dependencies transitioning between
all the random variables 8.

“Hidden”
is the term represents that an observer can view only the output states. But he
doesn’t realise the underlying sequence of transitions and states by which the
output is generated 9.

P (qjD) is the
output distribution of any document D. It is set to be the sample distribution
on words appearing in that document. For any document Dk, we can explicitly set

It is the
distribution that has the maximum probability of producing Dk itself by
repeatedly sampling the state “General English”. It is estimated by

The sum here is
taken for all documents present in the corpus. Using the parameters estimated
above, the formula for P (QjDk is R) is stated as under:

IV. ADVANTAGES

1. Hidden Markov
models (HMMs) are a formal foundation used for creating probabilistic models for
problems of linear sequence ‘labelling’. Just by drawing an intuitive image, a
conceptual toolkit is provided. This is very useful for building complex models.
They are at the hub of a set of miscellaneous programs. These programs include
gene finding, multiple alignments of sequence, profile searches and identification
of regulatory site.

2. An HMM is a
full probabilistic model—the overall ‘scores’ generated for sequences and the
parameters calculated are all probabilities 6, 9. Hence, Bayesian probability
theory can be incorporated for the manipulation of these numbers in more
powerful ways. This includes optimization of parameters and interpretation of
the significance of scores 5.

3. HMMs can be proved
useful for modelling of processes which contain different stages that occur in
definite orders 9.

If, for example,
you want to model the behaviour of a technical system that first boots, then
operates, then enters sleep mode, and iteratively changes between sleep and
operation later on, you might need three states (boot, operate, sleep) and can
use this process model to find out what’s going on in the system at any one
time. Similar is the case with a human biological system where the observations
can be the sequence of symptoms of a human being. Human genome project also
requires the assimilation of HMM for DNA sequencing and RNA structuring 10.

V. CHALLENGES

Complications
like scalability and frequencies of paging update are familiar IR issues. Ranking
algorithms are implemented with the usage of methods that elucidate relations
between the given query and the accumulated documents. All the feedback given
by the information retrieval system has to be evaluated, which is another issue
with IR. The way the system behaves, may or may not meet the expectations of the
user. All the documents that are returned from the procedure may not be
relevant to a given query.

The way a user
interacts with the IR system is termed as Information needs. Retrieval of a lot
of information might be disruptive in a number of systems. On the other hand,
in another number of systems, not returning a complete set of relevant data may
be inadequate.

As experienced,
handling a set of voluminous information from the internet might be extremely difficult
because of the extremely large size of documents the server manages.

A thousand of
documents can be returned by a simple retrieval query. Many of those documents
are loosely related to the original criteria of retrieval. To deal with this,
an IR system is required to have a query management that is efficient enough as
well as contains a good level of ability in order to give weight as priority to
documents that are closer for relevance to the user query.

VI. CONCLUSION

In simple terms,
high precision relates to a higher degree of relevance than irrelevance
returned by an algorithm considerably, while high recall states that most of
the relevant results are returned by an algorithm.

For the
comparison of HMM with the traditionally used model, Indexing and searching
were performed followed by applying searching to query multiple words, and
successful results were generated.

Succeeding the
above, Tf.idf values were found, and their precision compared with the ranked
HMM values.

In the analysis
held for comparing tf.idf model with HMM, we find that the precision of HMM is
greater than that of values generated by tf.idf. Thus, HMM is capable of
retrieving more relevant data than tf.idf does.